Series 3, Number 28, Fall 2013.

Transcription

Groupe de Travail Européen “Aide Multicritère à la Décision”
Série 3, nº28, automne 2013.
Opinion Makers Section
Multi-Objective Optimization and Multi-Criteria
Decision Analysis in the Energy Sector (part I – MOO)
Carlos Henggeler Antunes (1,2) – e-mail: ch@deec.uc.pt
(1) Dept. of Electrical Engineering and Computers, University of
Coimbra, Polo II, 3030-290 Coimbra, Portugal
(2) INESC Coimbra, Rua Antero de Quental 199, 3000-033 Coimbra,
Portugal
Abstract - The energy sector has been a fertile ground for
the application of operational research (OR) models and
methods (Antunes and Martins, 2003). Even though
different concerns have been present in OR models to
assess the merit of potential solutions for a broad range of
problems arising in the energy sector, the use of multiobjective optimization (MOO) and multi-criteria decision
analysis (MCDA) approaches is more recent, dating back
from mid-late 1970s. The need to consider explicitly
multiple uses of water resource systems or environmental
aspects in energy planning provided the main motivation
for the use of MOO and MCA models and methods with a
special evidence in scientific literature since the 1980s.
The increasing need to account for sustainability issues,
which is inherently a multi-criteria concept, in planning
and operational decisions, the changes in the organization
of energy markets, the conflicting views of several
stakeholders, the prevalent uncertainty associated with
energy models, have made MOO and MCA approaches
indispensable to deal with complex and challenging
problems in the energy sector (Diakoulaki et al., 2005;
Antunes et al., 2014).
1. Introduction
The capability of reliable provision of energy to meet a
vast range of needs and requirements in residential,
services/commerce,
agriculture,
industrial
and
transportation sectors, is one of the most distinctive
features of modern developed societies. From supplying
power and heat to production systems to satisfying
heating, cooling, lighting, and mobility needs, energy is
pervasive in everyday life. Until mid 1970s, when an
energy crisis occurred caused by the peaking of oil demand
in major industrial nations and embargoes from producer
countries, energy planning was almost exclusively driven
by cost minimization models subject to demand
satisfaction and technology constraints. This paradigm, in
which per capita energy consumption was an index of a
European Working Group “Multiple Criteria Decision Aiding”
Series 3, nº 28 Fall 2013.
nation’s prosperity, started to change due to the energy
crises in the 1970s and also the growing concerns
regarding environmental impacts associated with the
energy life-cycle from extraction, including the depletion
of fossil resources, to end-use. Therefore, the merits of
energy plans and policies could not be judged by
considering just economic costs, but other evaluation
aspects such as reliability of supply, environmental
impacts, source diversification, etc., should be explicitly
taken into account to address energy problems in a societal
perspective. Although issues other than economic costs
were often present at the outset of some studies, usually
those concerns were then amalgamated into an overall cost
dimension by monetizing, for instance, environmental
impacts and energy losses, rather than operationalizing
those multiple, incommensurate and conflicting evaluation
axes as expressing distinct perspectives of the merits of
courses of action.
In this context, MOO and MCDA models and methods
naturally gained an increasing relevance and acceptance in
the appraisal of energy technologies and policies in a vast
range of energy planning problems at different decision
levels (strategic, tactical, operational) and timeframes
(from long-term planning to near real-time control). The
recognized need and advantages of explicitly using
multiple objectives/criteria not just provided a value-added
in exploring a larger range of possible decisions
embodying different trade-offs between the competing
axes of evaluation but also enabled a richer critical
analysis of potential solutions. Furthermore, this modeling
and methodological framework made possible to include
the preferences and interests of multiple stakeholders in a
coherent manner into the decision process, to increase
solution acceptance, and the several sources of uncertainty
at stake, to obtain more robust recommendations.
Two major trends may be identified, which have a strong
impact of MOO/MCA research and practice on the energy
sector: the increasing awareness of the need to ensure
sustainable development, in which energy provision plays
a key role, and the trend for liberalization and market
deregulation, at least in some industry segments. The
concern of sustainable provision of energy meeting the
present needs without compromising the ability of future
generations to meet their needs is inescapable in the
development of decision support models in the energy
sector. Sustainability is inherently a multi-criteria concept,
which makes MOO/MCA approaches indispensable to deal
with the complex and challenging problems arising in the
energy sector. The exploitation of energy resources must
be balanced with the threats of climate change, mitigation
of impacts on human health and natural ecosystems,
assessment of geo-political risks, etc., also recognizing the
uncertainty, the long-term and possible large-scale effects
of today’s energy decisions. Technologies that promote
sustainable energy include renewable energy sources, such
as hydroelectricity, solar energy, wind energy, wave power,
geothermal energy, and tidal power, and also those
designed to improve energy efficiency. Besides the
important investments at stake in several energy decisions,
these embody also complex and controversial issues
related to global and inter-generational effects for which
the MOO/MCA toolbag offers the methodological
instruments to reach balanced decisions due to their ability
to combine sound models and methods with subjective
judgments and perspectives of reality.
Løken, 2007; Wang et al. 2009; Alarcon-Rodriguez et al.,
2010; Antunes et al., 2014). Greening and Bernow (2004)
even advocated the implementation of several multicriteria methods in an integrated assessment framework for
the design of coordinated energy and environmental
policies.
In MOO, mathematical programming models are
developed consisting of multiple objective functions to be
optimized in a feasible region defined by a set of
constraints, with different types of decision variables
(binary, integer, continuous, etc.). In MCA a limited
number of courses of action (alternatives) are, in general,
explicitly known a-priori to be evaluated according to
multiple evaluation criteria, possibly organized as a
hierarchical criterion tree, and the performances of the
alternatives may be qualitatively and/or quantitatively
expressed using different types of scales (ratio, interval,
etc.) thus leading to a bi-dimensional impact matrix
(alternatives vs. criteria). MOO/MCA approaches are
essential for a thorough analysis of a vast range of
problems in the energy sector at different decision levels
and with different timeframes in order to generate usable
recommendations that balance multiple, conflicting and
incommensurate evaluation aspects.
For many years companies in the energy industry were
generally vertically integrated, although at different
degrees, i.e. owning generation plants, transmission and
distribution networks, and customer access equipment
including billing services. Most of these companies were,
and still are in several cases, state-owned with the aim of
protecting public interests in face of the essential nature of
provision of energy, namely gas and electricity. The main
aim of deregulation, whether involving or not
privatization, was encouraging competition in many areas
to curb economic inefficiencies associated with the
operation of energy monopolies. In general, in the (more or
less concentrated) wholesale electricity markets competing
generators offer their electricity production to retailing
companies, which then sell it to clients. In the retail
markets end-use clients are able to select their supplier
from competing retailing companies.
2. Multi-objective optimization models and methods
MOO models and methods have been used to deal with a
large variety of energy problems at different organizational
levels and timeframes. A common distinction is made
between long-term/strategic, operational and short-term
problems (Table 1).
Models and methods to address end-users’ demand
response to reduce peak demand and energy bill are
currently a challenging research area in which MOO/MCA
models and methods are being used. The technological
improvements enabling small-scale production of
electricity is also expected to introduce further changes in
the industry, since the prosumer (i.e., simultaneously
producer and consumer) will expectedly be able to manage
demand, have a local micro/mini generation facility (e.g., a
small wind turbine or photovoltaic panels), store energy in
static batteries or in an electric vehicle, and buy from or
sell electricity back to the grid. Therefore, new and
challenging decision contexts emerge at different energy
industry levels, which should balance economic efficiency,
environmental concerns, social interests, and technological
issues.
Planning
Longterm/Strate
gic
The first historical applications of MOO/MCA in energy
planning date back to the late 1970s, namely concerning
power generation expansion planning or the choice of sites
for nuclear and fossil-fired generation plants. As the
relevance of MOO/MCA models was recognized, a vast
amount of literature reported new models, algorithmic
approaches and real-world applications to several
problems, also witnessing the need to take duly into
account problem structuring techniques to shape problems
to be tackled and dealing with uncertainty with the aim to
obtain robust conclusions (Hobbs and Meier, 2000;
Greening and Bernow, 2004; Diakoulaki et al., 2005;
Typical
timeframe
Several yearsdecades
Operational
Months-years
Short-term
Hours-daysweeks
Examples of decisions
to be made
Generation expansion
planning
Transmission facility
expansion
Siting of new power
plants
Energy-environmenteconomy models
Market design
Generation scheduling
Transmission
scheduling
Reactive
power
planning
Unit commitment
Power flow
Demand-side
management
Table 1. Categories of planning problems in power
systems according to the organizational level and
timeframe
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2.1. Power generation planning
objective functions consisting in aggregate indicators
penalizing the installed capacity and the energy output.
Besides considering conventional and renewable supplyside options, some models adopt a broader perspective of
integrated resource planning by also including demandside options in the planning process, which are aimed at
shaping the load diagram in such a way that peaks are
flattened and valleys are filled.
Power generation capacity expansion planning was one of
the first problems to be addressed using MOO, initially as
an extension of single objective cost minimization models.
As environmental issues gained an increasing attention,
models began to include them as explicit objective
functions rather than encompassing them in an overall cost
function by using, for instance, monetized pollutant
emissions associated with power generation. In these
problems the aim is, in general, identifying the amount of
power to be installed (number and type of generating units,
that is, primary energy source and energy conversion
technology, sometimes also involving siting decisions) and
output (energy to be produced by new and already installed
units) throughout a planning period, in general of a few
decades. With the development of renewable energy
resources, technologies for power generation expansion
involve coal units, large scale and small hydro units,
conventional and combined cycle natural gas units, nuclear
plants, wind farms, geothermal units, photovoltaic units,
etc.
The algorithmic approaches to tackle generation capacity
expansion planning models are very diversified and in
some way denote the trend from “classical” MOO
approaches, both generating methods to characterize as
exhaustively as possible the non-dominated solution set
and interactive methods using the preference information
expressed by decision makers/planners to guide and reduce
the scope of the search, to multi-objective meta-heuristics
and, in particular, multi-objective genetic/evolutionary
algorithms. The use of MOMILP models led to the
development of MOO algorithms based on branch-andbound or cutting planes, in general aimed at characterizing
the whole non-dominated solution set. In general, in
“classical” approaches a single non-dominated solution is
generated through the optimization of a surrogate
scalar(izing) function that temporarily aggregates the
original multiple objectives also including preference
information parameters, in such a way that the optimal
solution to this function is non-dominated to the MOO
model.
Population-based
meta-heuristics
(genetic/evolutionary
algorithms,
particle
swarm
optimization, differential evolution, etc.) are often
justified, besides the combinatorial complexity of the
problems, on the grounds that using a population of
solutions that expectedly converge to the true nondominated front (which is generally unknown) is more
efficient than resorting to the optimization of scalarizing
functions. Several sources of uncertainty are at stake in
these power generation expansion planning models,
namely concerning demand growth, primary energy prices,
inflows to hydro reservoirs, etc., and even regulations. The
uncertainty associated with the model coefficients is
usually modeled through stochastic coefficients or, in
fewer cases, fuzzy sets, and models are then tackled by
means of stochastic or fuzzy programming. Also, scenarios
to which a probability distribution is assigned are
sometimes used, those embodying sets of plausible
instantiations of uncertain model elements, such as the
ones mentioned above. The paradigm of robust solutions is
also used, in the sense that the variation in objective
functions, and even constraints, is within acceptable ranges
for uncertain model coefficients and parameters, thus
displaying a certain degree of “immunity” of solutions to
“moderate” changes in the inputs. Since these types of
prior incorporation of uncertainty in the mathematical
model leads, in general, to a significant additional
computational burden to obtain solutions, uncertainty may
be also tackled by performing (a posteriori) sensitivity
analysis of selected compromise solutions.
The objective functions include the minimization of the
total expansion cost (or just production costs), the
minimization of pollutant emissions (SO2, CO2, NOx,
etc.), the maximization of the system reliability/safety, the
minimization of outage cost, the minimization of
radioactive wastes produced, the minimization of the
external energy dependence of the country, the
minimization of a potential technical risk/damage
indicator, the minimization of option portfolio investment
risk, the minimization of fuel price risks, and the
maximization of employment at national or regional level.
The constraints mainly express generation capacity
lower/upper bounds, minimum load requirements,
satisfaction of forecasted demand including a reserve
margin, resource availability, technology restrictions due
to technical or political reasons (e.g., the amount of
nuclear power allowed to be installed), domestic fuel
quotas, energy security (as a surrogate for diversification
of the energy supply), committed power limits, budgetary
limitations, operational availability of generating units,
rate of growth of the addition of new capacity,
transmission constraints due to generation units placement,
coal/gas production and transportation capacities, need to
account for multiple water uses and capacity in hydro
reservoirs, pumping capacities. Multiple-use hydroelectric
systems, and in particular multi-reservoir cascaded
systems, impose additional issues to be considered, either
as objective functions or constraints, such as competition
of different operators on the same basin (scheduling of
reservoirs) and balancing energy and non-energy uses,
including dam safety, discharges and spills, flood
protection and control, agriculture irrigation, industrial and
domestic water supply, navigation, dilution of pollutants
and heated effluents, recreation, ecological sustainability
and protection of species, etc. Pollutant emissions
materialize either expressed as constraints in physical
quantities (tons), in general reflecting (national or
international) legislation, or (surrogate) environmental
Page 3
2.2. Network planning
generation, creates new challenges regarding distribution
network planning in a more dynamic stance taking into
account the integrated management of supply and demand
resources.
The network infrastructure (both transmission and
distribution) plays a critical role in providing energy to
consumers. When utilities were vertically integrated, thus
owning transmission network and generation assets, the
planning process was generally integrated. Being the sole
provider of services along the whole industry chain,
utilities had complete data and forecasting capability about
demand and its evolution. The complete knowledge about
decisions concerning the installation of new generation
units or the retirement of existing ones enabled also a more
controlled planning of the transmission network. Since
nowadays generation and transmission are usually
separated by means of functional unbundling or company
split, and due to competition in electricity generation,
transmission network planning is a more complex task.
Focusing on power networks, the transmission network has
a central position in system operations and wholesale
markets. Transmission network planning models are aimed
at determining the location, the size and the time frame of
the installation of new circuit additions to supply the
forecasted load throughout the planning period,
considering economic, environmental, technical and
quality of service objectives subject to operating
constraints given the existing network configuration and
generation units.
2.3. Reactive power planning
The reactive power compensation planning problem
involves determining the location and size of capacitors to
be installed in electrical distribution networks, which are
generally operated in a radial structure, to guarantee an
efficient delivery of active power to loads, releasing
system capacity, reducing system losses, and improving
bus voltage profile, thus promoting economic and
operational/quality of service benefits. This leads to nonlinear models with binary and continuous variables.
Objective functions generally express investment,
installation, and operation and maintenance costs, power
losses, economical operating conditions, system security
margin (line overloads due to excessive power flow),
voltage deviation from the ideal voltage at buses and
quality of service indicators. More recent methods to deal
with this problem are based on meta-heuristics to cope
with its combinatorial nature, namely population based
approaches devoted to MOO models, such as genetic
algorithms, particle swarm optimization and differential
evolution.
Aspects generally contemplated either as objective
functions
or
constraints
are:
economic
–
construction/reinforcement costs, equipment (transformer
stations, protection devices, etc.) upgrade costs, congestion
costs, energy losses costs, regional or national economic
growth induced by projects, facilitating competitive
wholesale markets; environmental - impacts of line
corridors, effects on location of power plants, need to
account for remote disperse renewable generation;
technical - network topology, inter-control area flows,
reliability standards associated with thermal, voltage and
stability requirements; quality of service indicators –
system/customer average interruption frequency/duration
indices, momentary average interruption frequency index;
public health - population exposure to electromagnetic
fields. Distribution networks carry electric energy from
transmission networks to customers. Distribution Network
Operators are generally organized on a geographical basis
and should provide a reliable operation complying with
technical and quality of service parameters, taking into
account the dynamics of end-use loads at different time
frames. The network distribution planning should also
support the operation of electricity market by enabling
non-discriminatory access to the network. The introduction
of dispersed renewable generation, sometimes at the
distribution network level, is changing the distribution
network planning process since this now needs to
accommodate not just traditional and new loads (for
instance, the electric vehicle) but also micro- and minigeneration facilities. The ongoing evolution to smart grids,
offering the technological basis using sophisticated
Information and Communication Technologies to
accommodate responsive demand, storage, and local
2.3. Unit commitment
The unit commitment problem consists in scheduling
generating power plants to be on, off, or in stand-by mode,
within a planning period to meet demand load. When the
power system is vertically integrated, unit commitment is
carried out by the utility in a centralized manner and the
objective function is minimizing overall costs subject to
meeting demand and reserve margins. When generation is
under competition, a generation company must decide
locally the unit commitment plan in order to maximize its
profit taking into account established power contracts and
the energy it estimates it may sell in a competitive (spot)
market according to price forecasts. Technical constraints
such as capacity constraints, stable operating levels,
minimum time period the unit is up and/or down, or
maximum rate of ramping up or down should be included
in mathematical models.
Economic dispatch problems consist in determining the
optimal combination of power output of online generating
power plants to minimize the total fuel cost while
satisfying load demand and operational constraints. Since
load demand can vary swiftly, dispatch should be able to
react and adapt while guaranteeing adequate cost or profit
levels, considering technical issues such as voltage control,
congestion, transmission losses, line overloading, voltage
profile, deviations of technical indicators from standard
values. Also, particular market structures should be taken
into account. While a generation company in a competitive
environment intends to maximize profits, entities such as
an independent system operator aims at maximizing social
Page 4
welfare, and these perspectives should be reconciled in
decision aid models. Economic-environmental dispatch
generally leads to MOO models in which cost
minimization, or profit maximization, and environmental
impact minimization (namely harmful emissions
originated at fossil-fuel power plants) are considered.
2.6. Energy markets
The liberalization of energy markets is aimed at increasing
overall efficiency through the introduction of competition
in some of the industry branches, namely generation and
retailing. The underlying idea is that by enhancing
efficiency and productivity gains, lower energy (namely
electricity) prices and lower production costs are achieved.
This trend of energy markets should go in line with
security of supply (by minimizing risks and overall
impacts of supply disruptions, diversifying energy sources
including renewables and energy efficiency), competitive
energy systems (to minimize energy costs for consumers
and industry, thus contributing to social policies and
economic competitiveness), and environmental protection
(thus minimizing the impacts of energy generation and use
on populations and ecosystems). Issues such as the
internalization of external costs to the environment into
energy prices, in accordance with the polluter pays
principle, are also at stake in designing market-based
mechanisms balancing multiple objectives, such as taxes
or tradable emission permits.
2.4. Demand side management
Demand-side resources have been used by utilities with the
main goals of achieving cost reduction and operational
benefits (such as reducing peak power demand, improving
reliability, increasing load factor, or reducing losses),
which maintain their potential attractiveness even in an
unbundled electricity industry. Appropriate power
curtailment actions impose changes on the regular working
cycles of loads to reduce peak demand without
compromising the quality of the energy services provided,
either by interrupting loads through direct load control or
voluntary load shedding, shifting their operation cycles to
other time periods or changing operating settings (such a
thermostats). The goal is to design and select adequate
load management actions, considering a comprehensive set
of objectives of different nature (economic, technical,
comfort, quality of services) and different players in the
power industry. Since the energy service provided by loads
under control is changed, possibly postponed or even not
provided at all, when load management actions are
implemented, attention should be paid to discomfort
caused to customers so that those actions become also
interesting for them, namely due to the ensuing reduction
in
their
electricity
bill.
Therefore,
multiple
incommensurate and conflicting objectives of economical,
technical and quality of service nature are at stake in the
design and selection of load management actions. These
aspects are modeled as either hard or soft constraints (by
establishing thresholds whose violation is included into a
penalty function).
References
Alarcon-Rodriguez, A., G. Ault, S. Galloway, Multiobjective planning of distributed energy resources: A
review of the state-of-the-art, Renewable and Sustainable
Energy Reviews, Vol. 14, nº 5, 1353–1366, 2010.
Antunes, C. H., A. G. Martins (Eds.), OR Models for
Energy Policy, Planning and Management, Annals of
Operational Research, Vols. 120/121, 2003.
Antunes, C. H., C. O. Henriques, Multi-Objective
Optimization and Multi-Criteria Analysis Models and
Methods for Problems in the Energy Sector. In: Multiple
Criteria Decision Analysis, J. Figueira, S. Greco, M.
Erghott (Eds.). Springer, 2014. (forthcoming)
2.5. Energy-economy-environment interactions
Diakoulaki, D., C. H. Antunes, A. G. Martins. In "Multiple
Criteria Decision Analysis - State of the Art Surveys", J.
Figueira, S. Greco, M. Erghott (Eds.). Int. Series in
Operations Research and Management Science, Vol. 78,
859-897, Springer, 2005.
The study of the interactions between the economy (at
national or regional levels), the energy sector and the
corresponding impacts on the environment inherently
involves multiple axes of evaluation of distinct policies. In
general, MOO models for this purpose are developed
based on input-output analysis (IOA) or general
equilibrium models (GEM). The analytical framework of
IOA enables to model the interactions between the whole
economy and the energy sector, thus identifying the energy
required for the provision of goods and services in an
economy and also quantifying the corresponding pollutant
emissions. GEM include interrelated markets and represent
the (sub-)systems (energy, environment, economy) and the
dynamic mechanisms of agent’s behavior to compute the
competitive market equilibrium and determine the optimal
balance
for
energy
demand/supply
and
emissions/abatement.
Greening, L., S. Bernow, Design of coordinated energy
and environmental policies: use of multi-criteria decisionmaking, Energy Policy, Vol. 32, nº 6, 721-735, 2004.
Hobbs B, P. Meier, Energy decisions and the environment:
a guide to the use of multicriteria methods. In:
International series in operations research and management
science. Kluwer Academic Publishers, 2000.
Løken, E., Use of multicriteria decision analysis methods
for energy planning problems, Renewable and Sustainable
Energy Reviews, Vol. 11, nº 7, 1584-1595, 2007.
Wang, J.-J., Y.-Y. Jing, C.-F. Zhang, J.-H. Zhao, Review on
multi-criteria decision analysis aid in sustainable energy
Page 5
decision-making, Renewable and Sustainable Energy
Reviews, Vol. 13, nº 9, 2263-2278, 2009.
The objective of the group, which is very active in applied
research, is to investigate the role of Multicriteria Decision
Aiding in decision processes related to environmental
sustainability. Starting from real-world problems, the focus
of the research concerns the experimentation of MCDA
methods with the objective of supporting the Decision
Makers in solving complex choices about territorial
projects, considering the full range of aspects of the
decision problem and the opinion of the different
stakeholders that can be affected by the project.
Many MCDA methods have been considered by the
research group over the years, including Analytic
Hierarchy Process/Analytic Network Process (AHP/ANP),
Dominance-based Rough Set Approach (DRSA),
Multicriteria – Spatial Decision Support System (MCSDSS), Choquet integral, Non Additive Robust Ordinal
Regression (NAROR), Electre III and Multi Attribute
Value Theory (MAVT). In most of these experimentations,
the research group made use of the collaboration with the
Higher Institute on Territorial Systems for Innovation
(SiTI, www.siti.polito.it) which provided numerous case
studies and expert knowledge for the research of the group.
MCDA Research Groups
Environmental
Decision Aiding
sustainability
and
Multicriteria
by Marta Bottero and Valentina Ferretti
Department of Urban and Regional Studies and Planning, Politecnico di
Torino, Torino, Italy
marta.bottero@polito.it; valentina.ferretti@polito.it
The research group on “Environmental sustainability and
Multicriteria Decision Aiding” is composed by Dr. Marta
Bottero, Dr. Valentina Ferretti and Professor Giulio
Mondini. The group works at the Department of Urban and
Regional Studies and Planning of the Politecnico di Torino
(Italy) in the general scientific domain of Project Appraisal
and Planning Evaluation. Having backgrounds in
Environmental Engineering and Sustainable Architecture,
the main interests of the group are related to sustainability
assessment and environmental decision-making in the
context of projects, plans and programmes, with multiple
research directions in MCDA. Overall, the work is inspired
by real life applications.
It is well known that sustainable development is a
multidimensional concept which includes socio-economic,
ecological, technical and ethical perspectives. Decision
problems in the domain of sustainability assessment
represent “weak” or unstructured problems since they are
characterized by multiple actors, many and often
conflicting values and views, a wealth of possible
outcomes and high uncertainty (Prigogine, 1997; Simon,
1969). Under these circumstances, the evaluation of
alternative projects is therefore a complex decision
problem where different aspects need to be considered
simultaneously, taking into account both technical
elements, which are based on empirical observations, and
non technical elements, which are based on social visions,
preferences and feelings.
Projects, plans and programmes are subject to specific
evaluation procedures, which aim at assessing the overall
sustainability of the proposed solutions. In this context,
mention can be made to the Environmental Impact
Assessment (EIA) and Strategic Environmental
Assessment (SEA), which are defined at the European
level by the Directives 1997/11/EC and 2001/42/EC,
respectively. Both EIA and SEA over time have
increasingly considered not only the environmental effects
of plans and projects, but also social and economic effects.
In this context, it has been noticed that neither an
economic reductionism nor an ecological one is possible
(Munda, 2005). Since in general, economic sustainability
has an ecological cost and ecological sustainability has an
economic cost, an integrative evaluation framework is
needed for tackling sustainability issues properly.
As far as AHP/ANP methodology is considered, the
experimentation was developed in the field of architecture
and environmental engineering. Mention has to be made to
the fact that the group followed the research work
developed in the past years by prof. Riccardo Roscelli
from the Politecnico di Torino in the field of AHP and
regional and urban planning. Particularly, the research
group made use of the methodology for leading the choice
of alternative projects for the construction of a skyscraper
in Torino or for addressing the selection of the most
sustainable technology in the case of waste water
management. It is also interesting to recall the application
of this theory in the domain of spatial planning where the
ANP was proposed for supporting the participative
procedure related to the development of a Municipal Plan.
With specific reference to the ANP theory, joint research
works were developed with the collaboration of two
colleagues of the same Department, Dr. Isabella Lami and
prof. Patrizia Lombardi, with different applications
especially in the field of transport planning.
With reference to the DRSA theory, the research has been
conducted with the collaboration of prof. Salvatore Greco
from University of Catania and other colleagues from the
same Department, Dr. Isabella Lami and Dr. Francesca
Abastante. In this case, the investigation considered the
application of the DRSA theory in different domains,
including transport planning and undesirable facilities
location. The results of the applications showed that DRSA
offers a useful tool for reasoning about the data involved in
the decision problem at hand and that it is suitable to
elicitate the DMs’ preferences and to support them by
explaining and justifying the final choice.
Another research direction of our group refers to the
development and application of Multicriteria- Spatial
Decision Support Systems. The integrated approach based
on the combination between Multicriteria Analysis and
Page 6
Geographic
Information
Systems
to
support
Environmental Impact Assessment and Strategic
Environmental Assessment procedures has been the focus
of the Ph.D. thesis of Valentina Ferretti who has tested the
approach in both the context of undesirable facilities
location (i.e. landfill and incinerator plants) and ecological
connectivity analysis. Recent developments of her
research, in a joint effort together with Dr. Gilberto
Montibeller (London School of Economics) are trying to
formalize the main challenges which characterize spatial
multicriteria evaluation. The Turin research group has also
collaborated with Dr. Elena Comino and Dr. Maurizio
Rosso, two colleagues from the Department of
Environment, Land and Infrastructure Engineering, and
Dr. Silvia Pomarico, a former Ph.D. student, for the
application of this integrated approach to study
biodiversity conservation and ecosystems value.
direction would study in depth the use of Spatial
Multicriteria Analysis for supporting the design of
territorial transformation scenarios. Through the adoption
of an approach of the type “value-focused thinking” future
studies would be directed towards the use of stakeholders
analysis, participation procedures and group decisionmaking.
Most relevant publications
Abastante F., Bottero M., Greco S., Lami I.M. (2013),
Dominance-based Rough Set approach and Analytic
Network Process for assessing urban transformation
scenarios. International Journal Of Multicriteria Decision
Making, 3 (2/3), 212-235.
Abastante F., Bottero M., Greco S., Lami I.M. (2012), A
Dominance-based rough set approach model for selecting
the location for a municipal solid waste plant,
Geoingegneria Ambientale e Mineraria, 137 (3), 45-56.
Working in the field of environmental sustainability
assessments, the decision problems that we are analysing
are often characterized by interacting elements (i.e. the
environmental components such as air, water, land, etc.).
Being interested in investigating these interactions, we
studied the Choquet Integral approach and we applied it in
the context of undesirable facilities management and
location (e.g. the requalification of an abandoned quarry
and the selection of the most compatible site for a new
incinerator plant). Following this interest, we started
investigating the Non Additive Robust Ordinal Regression
(NAROR) together with Isabella Lami from our
Department and a research group at the University of
Catania coordinated by Professor Salvatore Greco and
including Dr. Salvatore Corrente and Dr. Silvia Angilella.
With reference to this last methodological approach,
mention has to be made to the fact that we developed the
first real application of the NAROR and implemented it
for the location of an urban waste landfill in Northern
Italy.
Bottero M., Comino E., Duriavig M., Ferretti V., Pomarico
S. (2013), The application of a Multicriteria Spatial
Decision Support System (MCSDSS) for the assessment of
biodiversity conservation in the Province of Varese (Italy),
Land Use Policy, Vol. 30, pp. 730-738.
Bottero M., Comino E., Riggio V. (2011), Application of
the Analytic Hierarchy Process and the Analytic Network
Process for the assessment of different wastewater
treatment systems. Environmental Modelling & Software,
26 (10), 1211-1224.
Bottero M., Ferretti V. (2011), An Analytic Network
Process-based Approach for Location Problems: the Case
of a New Waste Incinerator Plant in the Province of Torino
(Italy). Journal Of Multicriteria Decision Analysis, 17 (34), 63-84.
Speaking about criteria interaction, the very recent
research of the group are focusing on the study and real
application of the extension of the ELECTRE III method
to take into account interactions between criteria. In this
case, the group is collaborating with Professor Bernard
Roy (LAMSADE, Paris), Professor José Rui Figueira
(University of Lisbon) and Professor Salvatore Greco
(University of Catania, Italy) for testing this innovative
approach in the context of environmental decision-making
problems.
Bottero M., Ferretti V. (2010), Integrating the Analytic
Network Process (ANP) and the Driving force- PressureState-Impact- Responses (DPSIR) Model for the
Sustainability Assessment of Territorial Transformations.
Management of Environmental Quality, 21 (5), 618-644.
Bottero M., Ferretti V., Figueira J., Greco S., Roy B., An
extension of Electre III for dealing with a multiple criteria
environmental problem with interaction effects between
criteria (under preparation).
Another research direction that the group is now
considering is related to the study of the Multi- Attribute
Value Theory. In this context, the method has been tested
in two participative contexts referring to the requalification
of a downgraded urban area and to the management of the
cultural heritage in the Province of Turin.
Bottero M., Ferretti V., Mondini G. (2013), Multi Attribute
Value Theory for decision-making in sustainable urban
planning projects, Civil Engineering and Environmental
Systems (under review).
Bottero M., Ferretti V., Mondini G. (2013), A Choquet
integral - based approach for assessing the sustainability of
a new waste incinerator, International Journal of
Multicriteria Decision Making, Vol.3, n. 2/3, pp. 157-177.
Future research lines of the group would further
investigate the existence of interaction among criteria with
specific reference to the use of environmental and
landscape indicators and indexes. Another research
Page 7
Bottero M., Ferretti V., Pomarico S. (2013), Assessing
different possibilities for the reuse of an open-pit quarry
using the Choquet integral, Journal of Multi-Criteria
Decision Analysis (accepted for publication).
Forum
Bottero M., Ferretti V., Pomarico S. (2012), Assessing the
sustainability of alternative transport infrastructures,
International Journal of the Analytic Hierarchy Process,
Vol. 4, Issue 1, pp. 61-77.
Michael Doumpos1, Constantin Zopounidis1, 2
The Robustness Concern in Preference Risaggregation
Approaches for Decision Aiding
1 Technical University of Crete, Dept. of Production Engineering and
Management, Financial Engineering Laboratory, University Campus,
73100 Chania, Greece
Bottero M., Lami I.M. (2010), Analytic Network Process
and sustainable mobility: an application for the assessment
of different scenarios. Journal of Urbanism, 3 (3), 275-293.
2 Audencia Group, School of Management, Nantes, France
INTRODUCTION
Bottero M., Mondini G (2008), An appraisal of analytic
network process and its role in sustainability assessment in
Northern Italy. Management of Environmental Quality, 19
(6), 642-660.
Similarly to other operations research/management science
modeling approaches, MCDA techniques are also based on
assumptions and estimates on the characteristics of the
problem, the aggregation of the decision criteria, and the
preferential system of the decision-maker (DM). Naturally,
such assumptions and estimates incorporate uncertainties
and errors, which affect the recommendations provided to
the DM. Thus, changes in the decision context, the
available data, or a reconsideration of the decision criteria
and the goals of the analysis, may ultimately require a very
different modeling approach leading to completely
different outputs. Therefore, even if the results may be
judged satisfactory when modeling and analyzing the
problem, their actual implementation in practice often
leads to new challenges not taken previously into
consideration.
In this context, robustness analysis has emerged as a major
research issue in MCDA. Robustness analysis seeks to
address the above concerns through the introduction of a
new modeling paradigm based on the idea that the
multicriteria problem structuring and criteria aggregation
process should not be considered in the context of a welldefined, strict set of conditions, assumptions, and
estimates, but rather as a process that aims towards
providing satisfactory outcomes even in cases where the
decision context is altered.
Vincke (1999) emphasized that robustness should not be
considered in the restrictive framework of stochastic
analysis and distinguished between robust solutions and
robust methods. He further argued that although robustness
is an appealing property, it is not a sufficient condition to
judge the quality of a method or a solution. Roy (2010), on
the other hand, introduced the term robustness concern to
emphasize that robustness is taken into consideration a
priori rather than a posteriori (as is the case of sensitivity
analysis). In the framework of Roy, the robustness concern
is raised by vague approximations and zones of ignorance
that cause the formal representation of a problem to
diverge from the real-life context.
The robustness concern is particularly important in the
context of the preference disaggregation approach (PDA)
of MCDA, which is involved with the inference of
preferential information and decision models from data
(Jacquet-Lagrèze and Siskos, 2001). Disaggregation
techniques facilitate the construction of multicriteria
evaluation models, using decision examples that the DM
provides, without requiring the specification of complex
Bottero M., Peila D. (2005), The use of the Analytic
Hierarchy Process for the comparison between
microtunnelling and trench excavation. Tunnelling and
Underground Space Technology, 20 (6), 501-513.
Ferretti V. (2011), A Multicriteria- Spatial Decision
Support System (MC-SDSS) development for siting a
landfill in the Province of Torino (Italy), Journal of MultiCriteria Decision Analysis, Vol. 18, pp. 231-252 (prizewinning paper for the Wiley Practice prize at the
International Conference on Multiple Criteria Decision
Making, June 2011, Jyväskylä, Finland).
Ferretti V., Bottero M., Mondini G. (2013), Decision
making and cultural heritage: an application of the Multi
Attribute Value Theory for the reuse of historical
buildings, Journal of Cultural Heritage (under review).
Ferretti V., Pomarico S. (2013), Ecological land suitability
analysis through spatial indicators: an application of the
Analytic Network Process technique and Ordered
Weighted Averaging approach, Ecological Indicators, Vol.
34, pp. 507-519.
Ferretti V., Pomarico S. (2012), An integrated approach for
studying the land suitability for ecological corridors
through spatial multicriteria evaluations, Environment,
Development and Sustainability, Vol. 15, n. 3, pp. 859-885.
Ferretti V., Pomarico S. (2012), Integrated sustainability
assessments: a spatial multicriteria evaluation for siting a
waste incinerator plant in the Province of Torino (Italy),
Environment, Development and Sustainability, Vol. 14,
Issue 5, pp. 843-867.
Page 8
parameters whose concept is not clearly understood by the
DM. In particular, using as input a set of decision
examples involving the DM’s evaluation of some reference
alternatives (reference set), disaggregation approaches use
optimization formulations to infer the parameters of a
decision model such that the outputs of the model are
consistent with the DM’s evaluations on the reference set.
This is a regression-based approach, in which the
robustness of the inferred model and the obtained
recommendations depend on a number of issues related to
the specification of the reference set and the form of the
decision model.
ROBUSTNESS
APPROACHES
ISSUES
IN
APPROACHES FOR ROBUST DISAGGREGATION
ANALYSIS
The research in the area of building robust multicriteria
decision models and obtaining robust recommendations
with disaggregation techniques can be classified into three
main directions. The first, involves approaches that focus
on describing the set of feasible decision models with
analytic or simulation techniques. The second focuses on
procedures for formulating robust recommendations
through multiple acceptable decision models, whereas a
third line of research has focused on techniques for
selecting the most characteristic (representative) model
from the set of all models compatible with the information
provided by the reference set. The following subsections
discuss these approaches in more detail.
DISAGGREGATION
The quality of models resulting from disaggregation
techniques is usually described in terms of their accuracy,
which can be defined as the level of agreement between
the DM’s evaluations and the outputs of the inferred
model. Except for accuracy-related measures, however, the
robustness of the inferred model is also a crucial feature.
Recent experimental studies have shown that robustness
and accuracy are in fact closely related (see for example,
Vetschera et al., 2010). However, accuracy measurements
are done ex-post and rely on the use of additional test data,
while robustness is taken into consideration ex-ante, thus
making it an important issue that is taken into
consideration before a decision model is actually put into
practical use.
The robustness concern in the context of PDA arises
because multiple alternative decision models can be
inferred in accordance with the information embodied in
the set of reference decision examples that a DM provides.
The diversity of the set of compatible decision models
depends on a number of factors, but the two most
important ones can be identified with respect to the
adequacy of set of reference examples and the complexity
of the selected decision modeling form. The former is
immediately related to the quality of the information on
which model inference is based. Vetschera et al. (2010)
performed an experimental analysis to investigate how the
size of the reference set affects the robustness and
accuracy of the resulting multicriteria models in
classification problems. They found that small reference
sets (e.g., with a limited number of alternatives with
respect to the number of criteria) lead to decision models
that are neither robustness nor accurate. Expect for its size
other characteristics of the reference set are also relevant,
such as the existence of noisy data, outliers, the existence
of correlated criteria, etc. (Doumpos and Zopounidis,
2002).
The complexity of the inferred decision model is also an
issue that is related to its robustness. Simpler models (e.g.,
a linear value function) are more robust compared to more
complex non-linear models. The latter are defined by a
larger number of parameters and as a result the inference
procedure becomes less robust and more sensitive to the
available data.
Describing the set of acceptable decision models
The DM’s evaluations for the reference alternatives
provide information on the set of acceptable decision
models that comply with these evaluations. Searching for
different solutions within this feasible set and measuring
its size provides useful information on the robustness of
the results. Analytic and simulation-based techniques have
been used for this purpose, focusing on convex polyhedral
sets for which the analysis is computationally feasible.
Jacquet-Lagreze and Siskos (1982) were the first to
suggest the analysis of multiple decision models
compatible with the DM’s evaluations (or even alternative
near-optimal ones in the cases of inconsistent reference
sets). The approach they suggested was based on a
heuristic post-optimality procedure seeking to identify
some characteristic alternative models corresponding to
corner points of the feasible polyhedron. Despite their
simplicity, post-optimality techniques provide only a
limited partial view of the complete set of models that are
compatible with the DM’s preferences. A more thorough
analysis requires the implementation of computationally
intensive analytic or simulation approaches. The latter
have gained much interest in the context of robust decision
aiding. Originally used for sensitivity analysis (Butler et
al., 1997) and decision aiding in stochastic environments
(Lahdelma and Salminen, 2001), simulation techniques
have been recently employed to facilitate the formulation
of robust recommendations under different decision
modeling forms. For instance, Tervonen et al. (2009) used
such an approach to formulate robust recommendations
with the ELECTRE TRI method, whereas Kadziński and
Tervonen (2013a, 2013b) used a simulation-based
approach to enhance the results of robust analytic
techniques obtained with additive value models in the
context of ranking and classification problems.
Robust decision aid with a set of decision models
Instead of focusing on the identification of different
evaluation models that can be inferred from a set of
reference decision examples through heuristic, analytic, or
simulation approaches, a second line of research has been
concerned with how robust recommendations can be
Page 9
formulated by aggregating the outputs of different models
and exploiting the full information embodied in a given set
of decision instances.
Siskos (1982) first introduced the idea of building fuzzy
preference relations based on a set of decision models
inferred with a preference disaggregation approach for
ordinal regression problems. Recently, this idea has been
further extended to consider not only a subset of
acceptable models but all models that can be inferred from
a given reference set, without actually identifying them.
This approach has been used for ordinal regression (Greco
et al., 2008) and classification problems (Greco et al.,
2010) with additive value function models, as well as with
and non-additive value models (Angilella et al., 2010) and
outranking models (Greco et al., 2011, Kadziński et al.,
2012, Corrente et al., 2013).
traditional optimization formulations on the grounds of the
regularization principle which is widely used in data
mining and statistical learning (Vapnik, 2000). On the
other hand, Bous et al. (2010) proposed a formulation for
ordinal regression problems that enables the construction
of an evaluation model through the identification of the
analytic center of the polyhedron form by the DM’s
evaluations on some reference decision instances. In a
different framework, Greco et al. (2011) considered the
construction of a representative model through an
interactive process, which is based on the grounds of
preference relations inferred from the full set of models
compatible with the DM’s evaluations. During the
proposed interactive process, different targets are
formulated, which can be used by the DM as criteria for
specifying the most representative evaluation model.
Selecting a representative decision model
CONCLUSIONS AND PERSPECTIVES
Having an analytic or simulation-based characterization of
all compatible models provides the DM with a
comprehensive view of the range of possible
recommendations that can be formed on the basis of a set
of models implied from some decision examples. On the
other hand, a single representative model is easier to use as
it only requires the DM to “plug-in”' the data for any
alternative into a functional, relational, or symbolic model.
Furthermore, the aggregation of all evaluation criteria in a
single decision model enables the DM to get insight into
the role of the criteria and their effect on the
recommendations formulated through the model.
In this context several approaches have been introduced to
infer a single decision model that best represents the
information provided by a reference set of alternatives.
Traditional disaggregation techniques such as the family of
the UTA methods (Siskos and Grigoroudis, 2010) use postoptimality techniques based on linear programming in
order to build a representative additive value function
defined as an average solution of some characteristic
models compatible with the DM’s judgments, defined by
maximizing and minimizing the criteria trade-offs. Such an
averaging approach provides a proxy of the center of the
feasible region. However, given that only a very few
number of corner points are identified with this heuristic
post-optimality process, the average solution is only a very
rough “approximation” of the center of the polyhedron.
Furthermore, the optimizations performed during the postoptimality analysis may not lead to unique results.
A number of alternative approaches have been proposed to
address the ambiguity in the results of the above postoptimality process. Beuthe and Scannella (2001) presented
different post-optimality criteria in an ordinal regression
setting to improve the discriminatory power of the
resulting evaluating model. Similar criteria were also
proposed by Doumpos and Zopounidis (2002) for
classification problems.
Alternative optimization formulations have also been
introduced allowing the construction of robust decision
models without requiring the implementation of postoptimality analyses. Following this direction, Doumpos
and Zopounidis (2007) presented modifications of
PDA techniques greatly facilitate the development of
multicriteria decision aiding models, requiring the DM to
provide minimal information without asking for the
specification of complex technical parameters which are
often not well-understood by DMs in practice. However,
using such a limited amount of data should be done with
care in order to derive meaningful and really useful results.
Robustness is an important issue in this context.
Addressing the robustness concern enables the formulation
of recommendations and results that are valid under
different conditions with respect to the modeling
conditions and the available data. In this article we briefly
outlined the main aspects of robustness in PDA and the
different lines of research that have been developed in this
area.
Future research should focus on the further theoretical and
empirical analysis of the robustness properties of PDA
formulations, the introduction of meaningful measures for
assessing robustness, the development of methodologies to
improve the robustness of models and solutions in decision
aid, and the exploitation of the recent developments in
areas such as robust optimization (Bertsimas et al., 2011).
REFERENCES
Angilella, S., Greco, S., Matarazzo, B., 2010. Non-additive
robust ordinal regression: A multiple criteria decision
model based on the Choquet integral. European Journal of
Operational Research 201, 277–288.
Bertsimas, D., Brown, D.B., Caramanis, C., 2011. Theory
and applications of robust optimization. SIAM Review 53,
464–501.
Beuthe, M., Scannella, G., 2001. Comparative analysis of
UTA multicriteria methods. European Journal of
Bous, G., Fortemps, P., Glineur, F., Pirlot, M., 2010.
ACUTA: A novel method for eliciting additive value
functions on the basis of holistic preference statements.
European Journal of Operational Research 206, 435–444.
Butler, J., Jia, J., Dyer, J., 1997. Simulation techniques for
the sensitivity analysis of multi-criteria decision models.
European Journal of Operational Research 103, 531–546.
Page 10
Corrente, S., Greco, S., S\lowi’nski, R., 2013. Multiple
criteria hierarchy process with ELECTRE and
PROMETHEE. Omega 41, 820–846.
Doumpos, M., Zopounidis, C., 2002. Multicriteria
Decision Aid Classification Methods. Kluwer Academic
Publishers, Dordrecht.
Doumpos, M., Zopounidis, C., 2007. Regularized
estimation for preference disaggregation in multiple
criteria decision making. Computational Optimization and
Applications 38, 61–80.
Greco, S., Kadziński, M., Słowiński, R., 2011. Selection of
a representative value function in robust multiple criteria
sorting. Computers & Operations Research 38, 1620–
1637.
Greco, S., Mousseau, V., Słowiński, R., 2008. Ordinal
regression revisited: Multiple criteria ranking using a set of
additive value functions. European Journal of Operational
Research 191, 416–436.
Greco, S., Mousseau, V., Słowiński, R., 2010. Multiple
criteria sorting with a set of additive value functions.
European Journal of Operational Research 207, 1455–
1470.
Greco, S., Kadziński, M., Mousseau, V., Słowiński, R.,
2011. ELECTRE-GKMS: Robust Ordinal Regression for
outranking methods. European Journal of Operational
Research 214(1):118-135.
Jacquet-Lagreze, E., Siskos, J., 1982. Assessing a set of
additive utility functions for multicriteria decision-making,
the UTA method. European Journal of Operational
Research 10, 151–164.
Jacquet-Lagrèze, E., Siskos, Y., 2001. Preference
disaggregation: 20 years of MCDA experience. European
Journal of Operational Research 130, 233–245.
Kadziński, M., Greco, S., Słowiński, R., 2012. Extreme
ranking analysis in robust ordinal regression. Omega
40(4):488-501.
Kadziński, M., Tervonen, T., 2013a. Robust multi-criteria
ranking with additive value models and holistic pair-wise
preference statements. European Journal of Operational
Research 228, 169–180.
Kadziński, M., Tervonen, T., 2013b. Stochastic ordinal
regression for multiple criteria sorting problems. Decision
Support Systems 55, 55–66.
Lahdelma, R., Salminen, P., 2001. {SMAA}-2: Stochastic
multicriteria acceptability analysis for group decision
making. Operations Research 49, 444–454.
Roy, B., 2010. Robustness in operational research and
decision aiding: A multi-faceted issue. European Journal of
Siskos, J., 1982. A way to deal with fuzzy preferences in
multicriteria decision problems. European Journal of
Siskos, Y., Grigoroudis, E., 2010. New trends in
aggregation-disaggregation approaches. In: Zopounidis,
C., Pardalos, P.M. (Eds.), Handbook of Multicriteria
Analysis. Springer, Berlin Heidelberg, pp. 189–214.
Tervonen, T., Figueira, J.R., Lahdelma, R., Dias, J.A.,
Salminen, P., 2009. A stochastic method for robustness
analysis in sorting problems. European Journal of
Vapnik, V.N., 2000. The Nature of Statistical Learning
Theory, 2nd ed. Springer, New York.
Vetschera, R., Chen, Y., Hipel, K.W., Marc Kilgour, D.,
2010. Robustness and information levels in case-based
multiple criteria sorting. European Journal of Operational
Research 202, 841–852.
Vincke, P., 1999. Robust solutions and methods in
decision-aid. Journal of Multi-Criteria Decision Analysis
8, 181–187.
Persons and Facts
In the 22nd International Conference on Multiple Criteria
Decision Making 17-21 June 2013 - Málaga (Spain), the
2013 awardees were:
- MCDM Gold Medal: Salvatore Greco.
- Edgeworth-Pareto Award: Constantin Zopounidis.
- Georg Cantor Award: João Climaco.
- MCDM Doctoral Disseration Award: Miłosz Kadziński
They are all members of the EURO Working Group on
MCDA.
Software
iMOLPe - A computational tool for teaching and
decision support in multi-objective linear programming
Carlos Henggeler Antunes and Maria João Alves
University of Coimbra / INESCC Coimbra, Portugal
ch@deec.uc.pt; mjalves@fe.uc.pt
Teaching optimization algorithms to engineering and
economics & management students requires, besides the
exposition to the essential concepts and methods, a handson approach by means of computational tools that enable
students to experiment and exert their critical thinking on
the results. This is particularly important in teaching multiobjective optimization since a “paradigm change” occurs
in the sense that an optimal solution “vanishes” and the
non-dominated solution set needs to be discovered,
hopefully in a constructive manner that may shed light on
the nature of the trade-offs involved.
Presenting a decision maker (DM) with a large set of nondominated solutions does not generally convey usable
information for actual decision support purposes.
Therefore, the involvement of the DM by providing
indications about his/her preferences is of outmost
importance to guide and reduce the scope of the search
process, thus minimizing both the computational effort
required for computing new solutions and guaranteeing
that these solutions are more in accordance with his/her
(evolving) preferences. This is accomplished in the
operational framework of interactive methods, which
intertwine computation steps and judgment steps thus
allowing for a progressive shaping of the DM’s
preferences as the selective characterization of the nondominated solution set unfolds.
Page 11
In this setting, the integration of different interactive
procedures of multi-objective linear programming
(MOLP), using different solution computation techniques,
search strategies, preference elicitation requirements,
visual interaction mechanisms and result displays, is the
most adequate tool to be offered to students approaching
these topics for the first time (after having been exposed
to, at least, single objective linear optimization). Our
experience shows that this is the best entrance door for
enhancing the students’ understanding of the main issues at
stake in multi-objective optimization, before progressing
to more technically demanding topics as integer and nonlinear optimization or approximate methods as metaheuristics.
We have developed a computational tool for teaching
purposes and decision support in MOLP problems. The
aim is to offer students in engineering and economics &
management an intuitive environment, in which the main
theoretical and methodological concepts of multi-objective
optimization can be apprehended through experimentation,
thus learning at their own pace. This tool can also be used
by academics and practitioners as a basis for
experimentation and decision support in MOLP models,
enabling to compute non-dominated solutions using
different strategies and providing different result displays
that interconnect the information that is being gathered.
This software, called Interactive MOLP Explorer –
iMOLPe, has been developed for Windows. It mainly
includes:
different scalarizing techniques for computing
non-dominated solutions, namely the weighted-sum, econstraint and reference point techniques, which can be
used individually or combined (for instance, by optimizing
a weighted-sum considering additional constraints on the
objective function values);
solution search strategies and visualization of
results originally implemented within the TRIMAP method
(Climaco and Antunes, 1987, 1989), such as the graphical
display of indifference regions on the weight space
(parametric diagram for the weighted-sum scalarization),
available for problems with two or three objective
functions; the graphical translation of bounds specified for
the objective function values onto the weight space; the
graphical display of the 2D or 3D objective function space,
showing the non-dominated extreme points (vertices)
already computed and the non-dominated edges
connecting adjacent non-dominated vertices;
the Step Method - STEM (Benayoun et al., 1971),
the Interval Criterion Weights - ICW (Steuer, 1977, 1986)
and the Pareto Race (Korhonen and Wallenius, 1988)
interactive methods;
an exact procedure to compute the nadir point
(Alves and Costa, 2009).
Although the software is intended to be mainly used as an
interactive explorer, it further includes a vector-maximum
algorithm (Steuer, 1986) that computes all efficient
extreme points to the MOLP problem. This algorithm can
also be used within the ICW interactive method, after the
contraction of the criterion cone, to compute a subset of all
efficient extreme points (the solutions that are attainable
with the reduced criterion cone).
A limited version of the software (with maximum problem
dimensions restricted to 6 objective functions, 100
decision variables and 100 functional constraints) is
available
for
download
from:
http://www.uc.pt/en/org/inescc/products. The package
includes a help file and a folder with problem examples.
References
Alves, M.J., J. P. Costa (2009). An exact method for
computing the nadir values in multiple objective linear
programming. European Journal of Operational Research,
198, 637-646.
Benayoun, R., J. de Montgolfier, J. Tergny, O. Larichev
(1971). Linear programming with multiple objective
functions:
step
method
(STEM).
Mathematical
Programming, vol. 1, 366-375
Climaco, J., C. H. Antunes (1987). TRIMAP - an
interactive tricriteria linear programming package.
Foundations of Control Engineering, vol. 12, 101-119.
Climaco, J., C. H. Antunes (1989). Implementation of an
user friendly software package - a guided tour of TRIMAP.
Mathematical and Computer Modelling, vol. 12, 12991309.
Korhonen, P., J. Wallenius (1988). A Pareto race. Naval
Research Logistics, vol. 35, 615-623.
Steuer, R. (1977). An interactive multiple objective linear
programming procedure. TIMS Studies in the Management
Sciences, vol. 6, 225-239.
Steuer, R. (1986). Multiple Criteria Optimization: Theory,
Computation and Application. Wiley.
Page 12
SCIENTIFIC
PROGRAM
/
PROGRAMME
SCIENTIFIQUE
Jeudi 24 octobre 2013 / Thursday 24th October 2013
About the 78th Meeting
On the 24, 25 and 26 October 2013, the 78th Meeting of
the European Working Group on Multiple Criteria
Decision Aiding was held at the Department of Economics
and Business of the University of Catania (Italy).
About 50 researchers participated to the meeting that had
as main theme “Multicriteria decision aiding in finance”
and was scheduled in six different sessions. A round table
on ‘’Multicriteria decision aiding and financial risk
evaluation” was organized on the first day. The
participants to the round table were Bernard Roy, Roman
Słowiński, Jaap Spronk and Constantin Zopounidis.
During those days were also presented the 79th edition of
the meeting to be held in Athens in Spring 2014 and in
Quebec, Fall 2014.
On Saturday, 26 October, 2013, the participants visited the
Greek theatre of Syracuse and did a tour of the city.
Note that during the meeting the sun has been always
shining and the weather has been pleasantly hot.
12h30 – 13h30 / 12:30pm – 1:30pm : Accueil des
participants et inscriptions / Welcome and registration of
participants
13h30 – 14h00 / 1:30pm – 2:00pm : Bienvenue à Catania /
Welcome to Catania (Salvatore Greco - Benedetto
Matarazzo)
14h00 – 16h00 / 2:00pm – 4:00pm : 1ère session / 1st
session
- Bernard Roy, José Rui Figueira, J. Almeida-Dias,
Discriminating thresholds as a tool to cope with imperfect
knowledge in multiple criteria decision aiding: Theoretical
results and practical issues
- Tuomas J. Lahtinen, Raimo P. Hämäläinen, Path
dependency in the Even Swaps Method
- Tommi Tervonen, Gert van Valkenhoef, Entropy-optimal
weight constraint elicitation with additive multi-attribute
utility models
- Yannis Siskos, Kostas Mastorakis, Eleftherios Siskos,
Multicriteria decision and Argumentation : Application to
The process of evaluating environmental projects
Travaux soumis à discussion / Paper submitted to
discussion :
- Fouad Ben Abdelaziz, Hatem Masri, Houda Alaya, A
Stochastic Goal Programming Approach for the
Multiobjective Stochastic Vehicle Routing Problem
- Alain Broutin, Vers une approche multicritère du jeu
d'entreprise "Forest"
- E. Grigoroudis, Y. Politis, Evaluating extensions of the
MUSA method for modeling additional preferences
- Silvia Angilella, Sebastiano Mazzù, The Financing of
Innovative SMEs: a multicriteria credit rating model
16h00 – 16h15 / 4:00pm – 4:15pm : Pause café / Coffee
break
Last but not least, on early Saturday morning Mount Etna
erupted above the town of Catania, spewing lava and
sending a plume of ash into the air ….
16h15 – 18h15 / 4:15pm – 6:15pm : 2ème session / 2nd
session :
Round table: L'aide multicritère à la décision et à
l'évaluation des risques en finance / Multiple Criteria
Decision Aiding and financial risk evaluations
(Bernard Roy, Roman Słowiński, Jaap Spronk, Constantin
Zopounidis)
Vendredi 25 octobre 2013 / Friday 12th Ocotober 2013
9h00 – 10h30 / 9:00am – 10:30am : 3ème session / 3rd
session :
- Marta Bottero, Valentina Ferretti, José Rui Figueira,
Salvatore Greco, Bernard Roy, An extension of Electre III
for dealing with a multiple criteria environmental problem
with interaction effects between criteria (40 minutes)
- Ky Vu, Horst Hamacher, Improved box representation of
Pareto sets and application to bicriteria multicommodity
network flows
Page 13
Travaux soumis à discussion / Papers submitted to
discussion :
- Panagiotis Manolitzas, Evangelos Grigoroudis, Nikolaos
Matsatsinis, MEDUTA II: Integrating Simulation
techniques and Stochastic UTA for the improvement of an
Emergency Department
- Luisa Sturiale, Maria Rosa Trovato, The DRSA as tool
for the social evaluation of the protection and the
enhancement environmental policies
- Antonio Boggia, Gianluca Massei, Measuring
sustainability using a GIS-multicriteria model
- Bastien Rizzon, Vincent Clivillé, Sylvie Galichet, Aide à
la décision pour les entreprises industrielles inscrites dans
une démarche de développement durable
- Georgios Samaras, Maria Giannoula, A Multicriteria
Approach for Selecting a Landfill Location
- Mauro Munerato, modeFRONTIER : the new MCDM
tool
- Salvatore Corrente, Salvatore Greco, Milosz Kadziński,
Roman Słowiński, Inducing probability distributions on
the set of value functions
16h30 – 16h45 / 4:30pm – 4:45pm : Pause café / Coffee
break
10h30 – 10h45 / 10:30am – 10:45am : Pause café / Coffee
break
16h45 – 18h15 / 16:45pm – 18:45pm : 6ème session / 6th
session :
- Adiel Almeida-Filho, An application of a MCDA model
in a supplier selection problem
- Alfio Giarlotta, The pseudo-transitivity of preference
relations: strict and weak (m, n)-Ferrers properties
- Sarah Ben Amor, Kaouthar Lajili ,Exploring the risk
tolerance in the gold industry: A multicriteria approach
- Willem K. M. Brauers, Romualdas Ginevicius, Selection
of shares for a shareholder by Multiple Objectives
Optimization
10h45 – 12h45 / 10:45am – 12:45am : 4ème session / 4th
session :
- Yves De Smet, A summary of recent researches about the
PROMETHEE methods
- Risto Lahdelma, Ian Durbach, Pekka Salminen, AHP and
Stochastic Multicriteria Acceptability Analysis
- Olivier Cailloux, Marc Pirlot, Descriptive models of
rational preferences
- Jasmin Tremblay, Irène Abi-Zeid, Multicriteria decision
and Argumentation : Application to the process of
evaluating environmental projects
discussion :
- Vassilios Christopoulos, Claudia Ceppi, Francesco
Mancici, Coastal vulnerability to climate change: a
coupled scenario analysis with multicriteria method: the
case of metropolitan area of Bari (Italy)
- Alfio Giarlotta, A genesis of interval orders and
semiorders: transitive NaP-preferences
- Maria Franca Norese, Giuliano Dall’O’, Annalisa
Galante, Chiara Novello, A Multicriteria Decision Aiding
Methodology to support Public Administration on
Sustainable Energy Action Plans
- Salvatore Corrente, Michael Doumpos, Salvatore Greco,
Roman Słowiński, Constantin Zopounidis, Applying
Multiple Criteria Hierarchy Process to UTADIS and
UTADISGMS
discussion :
- Zoe Nivolianitou, A GIS-based Decision Support System
for Land Use Planning
- Vasile Postolică, Dual Isac’s Cones
- Chiara D’Alpaos, Canesi R.1, Oppio A.2, MCDA
approaches in Real Estate Investment Decisions: A
Multidimensional Framework for Risk Assessment
- Salvatore Corrente, Salvatore Greco, Milosz Kadziński,
Roman Słowiński, Ordinal Regression and Robust Ordinal
Regression for non-monotonic value functions
12h45 – 14h15 / 12:45am – 2:15pm : Lunch
14h15 – 15h00 / 2 :15pm – 3:00pm : Vie du groupe de
travail et prochaines réunions / Group next meetings,
Zoe Nivolianitou, Athens meeting, spring 2014; Irène AbiZeid, Québec meeting, Autumn 2014.
20h30 / 8:30pm : Dîner de gala à Catania / Dinner in
Catania
15h00 – 16h30 / 3:00pm – 4:30pm : 5ème session / 5th
session :
Saturday 26th October 2013
- Excursions / Social activities
- Fabio Fantozzi, Fabio Spizzichino. On stochastic
dependence among targets components in the Target-Based
Approach to Utility Functions
- Miłosz Kadziński, Salvatore Corrente, Salvatore Greco,
Roman Słowiński, Preferential reducts and constructs in
robust multiple criteria ranking and sorting
- Maria A. de Vicente, Jaime Manera, Vincent Clivillé,
Lionel Valet, Parameter setting support for a 3D images
processing systems
discussion :
Page 14
http://www.icores.org
Forthcoming meetings
19-21/3/2014
11th ESICUP Meeting
Beijing, China
http://www.fe.up.pt/~esicup/extern/esicup-11thMeeting
6-8/1/2014
ISAIM 2014 - 13th International Symposium on Artificial
Intelligence and Mathematics
Fort Lauderdale, Florida
http://www.cs.uic.edu/Isaim2014/
30/3-2/4/2014
14th International Conference on Project Management and
Scheduling
Munich, Germany
http://www.pms2014.wi.tum.de/
8/1/2014
SOPS 2014 - NSF Workshop on Self-Organizing Particle
Systems
Portland, Oregon, USA
http://sops2014.cs.upb.de/
30/3-1/4/2014
2014 INFORMS Conference on Business Analytics and
Operations Research
Boston, MA, USA
http://meetings.informs.org/analytics2014/
12-17/1/2014
2nd International Optimisation Summer School
Kioloa Beach, New South Wales, Australia
http://optimisationmelbourne.wordpress.com/2013/09/07/s
ummer-school/
1-2/4/2014
7th Simulation Workshop (SW14)
Worcestershire, UK
http://www.theorsociety.com/SW14/
5-9/2/2014
EURO mini-conference on Optimization in the Natural
Sciences
Aveiro, Portugal
http://minieuro2014.web.ua.pt/
9-11/4/2014
APMOD 2014 - 11th International Conference on Applied
Mathematical Optimization and Modelling
Coventry, U.K.
http://www.apmod2014.org/
16-21/2/2014
LION 8: Intelligent optimization in Bioinformatics,
Biomedicine and Neuroscience
Gainesville, Florida, USA
http://caopt.com/LION8/index.php
14-16/4/2014
Statistical and Probabilistic Methodologies for Energy
Systems - CRISM Workshop
Warwick, U.K.
http://www.warwick.ac.uk/CRISM
17-28/2/2014
EURO PhD SCHOOL on MCDM - MULTRICRITERIA
DECISION MAKING WITH MATHEMATICAL
PROGRAMMING
Madrid, Spain
http://www.mat.ucm.es/imeio/cursos/EPS_MCDM
Spring, 2014
79th EWG on MCDA
Athens, Greece
28/2-4/3/2014
6th German Polish Conference on Optimization Methods
and Applications
Wittenberg, Germany
http://www.gpco2014.de/
14-17/4/2014
DEA2014 - 12th International Conference on Data
Envelopment Analysis
Kuala Lumpur, Malaysia
http://deaconference.com/dea2014
2-4/3/2014
INFORMS Telecommunications 2014
Lisbon, Portugal
http://www.informstelecom2014.fc.ul.pt
23-25/4/2014
EvoSTOC 2014 - (Evolutionary Algorithms /
Metaheuristics in Stochastic and Dynamic Environments)
Granada, Spain
http://www.evostar.org/cfpEvoSTOC.html
5-7/3/2014
ISCO 2014 - 3rd International
Combinatorial Optimization
Lisbon, Portugal
http://ISCO2014.fc.ul.pt
Symposium
23-25/4/2014
EvoCOP 2014 - 14th European Conference on
Evolutionary Computation in Combinatorial Optimisation
Granada, Spain
http://www.evostar.org/
on
6-8/3/2014
3rd International Conference on Operations Research and
Enterprise Systems
Angers, Loire Valley, France
1-3/5/2014
ECCO XXVII - CO 2014 Joint Conference
Munich, Germany
Page 15
http://www.ecco2014.ma.tum.de/
Georgetown University, USA
https://www.informs.org/Community/DAS/DASConference
9-10/5/2014
Symposium on Discrete Mathematics 2014
Frankfurt, Germany
http://www.math.uni-frankfurt.de/dm2014/
16-20/6/2014
ISOLDE 2014 - International Symposium On Locational
DEcision
Naples and Capri, Italy
http://www.isolde2014.org
19-22/5/2014
SIAM Conference on Optimization (OP14)
San Diego, California, USA
http://www.siam.org/meetings/op14/
23-25/6/2014
XVII - 17th Conference on Integer Programming and
Combinatorial Optimization
Bonn, Germany
http://www.or.uni-bonn.de/ipco/
19-23/5/2014
NIDISC'14 - 17th International Workshop on Nature
Inspired Distributed Computing
Phoenix, Arizona, USA
http://nidisc2014.gforge.uni.lu/
26-28/6/2014
EURO Working Group conference on Operational
Research in Computational Biology, Bioinformatics and
Medicine
Poznan, Poland
http://cbbm2014.cs.put.poznan.pl
19-23/5/2014
CPAIOR 2014 - Eleventh International Conference on
Integration of Artificial Intelligence and Operations
Research Techniques in Constraint Programming
Cork, Ireland
http://4c.ucc.ie/cpaior2014/
7-10/7/2014
PMAPS 2014 - International Conference on Probabilistic
Methods Applied to Power Systems
Durham, UK
http://www.dur.ac.uk/pmaps.2014
27-30/5/2014
ECMS 2014 - 28th European Conference on Modelling
and Simulation
Brescia, Italy
http://www.scs-europe.net/conf/ecms2014/index.html
IFORS 2014 - 20th Conference of the International
Federation of Operational Research Societies
Barcelona, Spain
http://www.ifors2014.org
29-31/5/2014
Computational Management Science 2014 - CMS2014
University of Lisbon, Portugal
http://cms2014.fc.ul.pt
4-6/6/2014
OPTI 2014 - Engineering
Optimization
Kos Island, Greece
http://www.opti2014.org
and Applied
19/7/2014-1/8/2015
EURO Summer Institute 2014. OR in Agriculture and
Agrifood Industry
Lleida, Spain
http://orafm.udl.cat/?page_id=233
Sciences
20-25/7/2014
WCCM 2014 - 11th World Congress on Computational
Mechanics
Barcelona, Spain
http://www.wccm-eccm-ecfd2014.org
5-7/6/2014
International IEEE Conference Logistics Operations
Management, GOL'14
Rabat, Morocco
http://www.ensias.ma/gol/
20-25/7/2014
ORAHS 2014 - Operational Research Applied to Health
Services
Lisbon, Portugal
http://www.orahs2014.fc.ul.pt
10-13/6/2014
GDN2014
Toulouse, France
http://www.irit.fr/gdn2014/
28-30/7/2014
Optimization 2014
Guimarães, Portugal
http://optimization2014.dps.uminho.pt
11-20/6/2014
EURO Summer Winter Institute “Operational Research
applied to health in a modern world”
Forte di Bard, Italy
http://orahs.di.unito.it/eswi.html
29-31/7/2014
6th International Conference on Applied Operational
Research - ICAOR 2014
Vancouver, Canada
16-18/6/2014
INFORMS Decision Analysis Society Conference 2014
Page 16
http://www.tadbir.ca
October 6-9, 2013
Minneapolis Convention Center & Hilton Minneapolis,
USA
http://www.informs.org
24-29/8/2014
19th IFAC World Congress IFAC 2014
Cape Town, South Africa
http://www.ifac2014.org/
The 36th Annual Meeting of the Society for Medical
Decision Making
October 19-24, 2014
Doral Golf Resort and Spa, USA
http://smdm.org/smdm_annual_meetings.shtml
26-29/8/2014
PATAT 2014 - 10th International Conference on the
Practice and Theory of Automated Timetabling
York, United Kingdom
http://www.patatconference.org/patat2014/
2-5/9/2014
International Conference on Operations Research, OR2014
Aachen, Germany
http://www.or2014.de
8-11/9/2014
EngOpt 2014 - 4th International
Engineering Optimization
Lisbon, Portugal
http://www.dem.ist.utl.pt/engopt2014
Conference
INFORMS Annual Meeting 2014 San Francisco
November 16-19, 2014
Hilton San Francisco, USA
http://www.informs.org
on
Announcements and Call for Papers
Multiple Criteria Decision Analysis on Financial
Lexicon, Constantin ZOPOUNIDIS
10-12/9/2014
ANTS 2014 - Ninth International Conference on Swarm
Intelligence
Brussels, Belgium
http://iridia.ulb.ac.be/ants2014
http://lexicon.ft.com/Term?term=multiple-criteriadecision-analysis
Public Decision Making and Decision Conferencing
(PLENARY
SPEAKER
Carlos
Bana,
22nd
International Conference on Multiple Criteria Decision
Making, Malaga, 2013)
24-26/9/2014
The 15th International Conference on Operational
Research KOI 2014
Osijek, Croatia
http://www.hdoi.hr/en/call-for-papers
http://web.ist.utl.pt/carlosbana/Plenary%20Carlos
%20Bana%20e%20Costa%20MCDM
%202013%20Malaga%2020_6_13.pdf
Autumn 2014
80th EWG on MCDA
Québec (Canada)
Web site for Annoucements and Call for
Papers:
www.cs.put.poznan.pl/ewgmcda
27-31/10/2014
META'2014 - International Conference on Metaheuristics
and Nature Inspired Computing
Marrakech, Morocco
http://meta2014.sciencesconf.org/
Call for Papers
Call for paper : Euro Journal of Decision Processes /
Special issue on Preference Elicitation and Learning
9-12/11/2014
INFORMS Annual Meeting 2014
San Francisco, USA
http://meetings2.informs.org/sanfrancisco2014/
Marc Pirlot and Vincent Mousseau are editing a feature
issue on preference elicitation and learning (see the
appended Call for Papers.
Deadline for the paper, the end of November.
See for more information
http://link.springer.com/journal/40070/1/1/page/1
3-7/8/2015
23rd International Conference on Multiple Criteria
Decision Making MCDM 2015
Hamburg, Germany
http://www2.hsu-hh.de/logistik/MCDM-2015/
IFORS 2014. Barcelona, Spain, July 13-18 2014
Abstract submissions opens: 1 November 2013
Abstract submissions closes: 31 January 2014
INFORMS Annual Meeting 2013 Minneapolis
Page 17
Early Registration: 1 November 2013- 28 February 2014
Regular Registration: 1 March - 30 April 2014
Late and on-site registration:1 May - 13 July 2014
Articles Harvest
The IFORS 2014 Triennial Conference will be held in
Barcelona, Spain,
July 13-18 2014.
(This section is prepared by Salvatore CORRENTE,
salvatore.corrente@unict.it)
Abbas, A.E. (2013). Utility copula functions matching all
boundary assessments. Operations Research, 61(2), 359371.
Contacts:
Elena Fernández. Organizing Committee Chair Stefan
Nickel.
Program Committee Chair
All attendees, including speakers and session chairs, must
register and pay the registration fee. If you need an early
confirmation for visa or budgetary reasons, please
contactinfo@ifors2014.org <mailto:info@ifors2014.org>.
For all other aspects, please contact the conference
secretariatifors2014@pacifico-meetings.com
http://www.ifors2014.org
Abounacer, R., Rekik, M., Renaud, J. (2014). An exact
solution
approach
for
multi-objective
locationtransportation problem for disaster response. Computers
and Operations Research, 41(1), 83-93.
Abrardo, A., Belleschi, M., Detti, P. (2013). Optimal radio
resources and transmission formats assignment in OFDMA
systems. Computers and Operations Research, 40(10),
2284-2300.
Books
Achillas, C., Aidonis, D., Vlachokostas, C., Folinas, D.,
Moussiopoulos, N. (2013). Re-designing industrial
products on a multi-objective basis: A case study. Journal
of the Operational Research Society, 64(9), 1336-1346.
Multi-criteria Decision Analysis: Methods and
Software
By Ishizaka Alessio, Nemery Philippe, Multicriteria
Decision Aid: Methods and software, Wiley, Chichester,
2013 ( ISBN: 978-1-1199-7407-9)
Adeyemi, S., Demir, E., Chaussalet, T. (2013). Towards an
evidence-based decision making healthcare system
management: Modelling patient pathways to improve
clinical outcomes. Decision Support Systems, 55(1), 117125.
http://eu.wiley.com/WileyCDA/WileyTitle/productCd1119974070.html
It describes the leading MCDA techniques: AHP, AHPSort,
GAHP, ANP, MAUT, UTA, UTA GMS, GRIP,
MACBETH, PROMETHEE I, PROMETHEE II,
PROMETHEE GDSS, FLOWSORT, GAIA, FS-GAIA,
ELECTRE III, ELECTRE-TRI, TOPSIS, GOAL
PROGRAMMIG, WEIGHTED GOAL PROGRAMMIG,
LEXOGRAPHIC GOAL PROGRAMMIG, CHEBYSHEV
GOAL PROGRAMMIG, DEA, DECISION DECK AND
DECERNS.
Adjarath Lemamou, E., Chamberland, S., Galinier, P.
(2013). A reliable model for global planning of mobile
networks. Computers and Operations Research, 40(10),
2270-2282.
Ahiska, S.S., Appaji, S.R., King, R.E., Warsing Jr., D.P.
(2013). A Markov decision process-based policy
characterization approach for a stochastic inventory
control problem with unreliable sourcing. International
Journal of Production Economics, 144(2), 485-496.
Ahonen, H., De Alvarenga, A.G., Amaral, A.R.S. (2014).
Simulated annealing and tabu search approaches for the
Corridor Allocation Problem. European Journal of
Operational Research, 232(1), 221-233.
Multi-Criteria
Decision
Analysis:
Environmental
Applications and Case Studies, published in 2012 by
CRC Press.
P. Vasant, N. Barsoum and Jeffrey Webb , Innovation in
Power, Control, and Optimization: Emerging Energy
Technologies
http://www.igi-global.com/book/innovation-powercontrol-optimization/52721
Through a collection of case studies, Multi-Criteria
Decision Analysis: Environmental Applications and
Case Studies gives readers the tools to apply cutting-edge
MCDA methods to their own environmental projects. It
offers an overview of the types of MCDA available and a
conceptual framework of how it is applied, with the focus
on its applicability for environmental science.
Aksoy-Pierson, M., Allon, G., Federgruen, A. (2013). Price
competition under mixed multinomial logit demand
functions. Management Science, 59(8), 1817-1835.
Aktaş, E., Özaydin, Ö., Bozkaya, B., Ülengin, F., Önsel, Ş.
(2013). Optimizing fire station locations for the Istanbul
Metropolitan Municipality. Interfaces, 43(3), 240-255.
Al Badawi, A., Shatnawi, A. (2013). Static scheduling of
directed acyclic data flow graphs onto multiprocessors
using particle swarm optimization. Computers and
Operations Research, 40(10), 2322-2328.
Page 18
Alcantud, J.C.R., de Andrés Calle, R., Cascón, J.M.
(2013). A unifying model to measure consensus solutions
in a society. Mathematical and Computer Modelling, 57(78), 1876-1883.
Angélica Salazar-Aguilar, M., Langevin, A., Laporte, G.
(2014). The multi-district team orienteering problem.
Computers and Operations Research, 41(1), 76-82.
Angilella, S., Corrente, S., Greco, S., Słowiński, R. (2014).
MUSA-INT: Multicriteria customer satisfaction analysis
with interacting criteria. Omega, 42(1), 189-200.
Alfandari, L., Sadki, J., Plateau, A., Nagih, A. (2013).
Hybrid column generation for large-size covering integer
programs: Application to transportation planning.
Antmann, E.D., Shi, X., Celik, N., Dai, Y. (2013).
Continuous-discrete simulation-based decision making
framework for solid waste management and recycling
programs. Computers and Industrial Engineering, 65(3),
438-454.
Ali, A.I., O'Connor, D.J. (2013). Using truck-inventorycost to obtain solutions to multi-period logistics models.
International Journal of Production Economics, 143(1),
144-150.
Aouni, B., Colapinto, C., La Torre, D. (2013). A
cardinality constrained stochastic goal programming
model with satisfaction functions for venture capital
investment decision making. Annals of Operations
Research, 205(1), 77-88.
Alidaee, B. (2014). Zero duality gap in surrogate
constraint optimization: A concise review of models.
European Journal of Operational Research, 232(2), 241248.
Allon, G., Deo, S., Lin, W. (2013). The impact of size and
occupancy of hospital on the extent of ambulance
diversion: Theory and evidence. Operations Research,
61(3), 544-562.
Arabameri, S., Salmasi, N. (2013). Minimization of
weighted earliness and tardiness for no-wait sequencedependent setup times flowshop scheduling problem.
Computers and Industrial Engineering, 64(4), 902-916.
Araya, I., Riff, M.-C. (2014). A beam search approach to
the container loading problem. Computers and Operations
Research, 43(1), 100-107.
Almeder, C., Hartl, R.F. (2013). A metaheuristic
optimization approach for a real-world stochastic flexible
flow shop problem with limited buffer. International
Araz, O.M., Lant, T., Fowler, J.W., Jehn, M. (2013).
Simulation modeling for pandemic decision making: A
case study with bi-criteria analysis on school closures.
Decision Support Systems, 55(2), 564-575.
Alpern, S., Lidbetter, T. (2013). Mining coal or finding
terrorists: The expanding search paradigm. Operations
Research, 61(2), 265-279.
Arikan, M., Deshpande, V., Sohoni, M. (2013). Building
reliable air-travel infrastructure using empirical data and
stochastic models of airline networks. Operations
Research, 61(1), 45-64.
Altin, A., Fortz, B., Thorup, M., Ümit, H. (2013). Intradomain traffic engineering with shortest path routing
protocols. Annals of Operations Research, 204(1), 65-95.
Amaral, A.R.S. (2013). A parallel ordering problem in
facilities layout. Computers and Operations Research,
40(12), 2930-2939.
Aristondo, O., Garcia-Lapresta, J.L., Lasso De La Vega,
C., and Marques Pereira, R.A. (2013). Classical inequality
indices, welfare and illfare functions, and the dual
decomposition. Fuzzy Sets and Systems, 228, 114-136.
Amailef, K., Lu, J. (2013). Ontology-supported case-based
reasoning approach for intelligent m-Government
emergency response services. Decision Support Systems,
55(1), 79-97.
Assis, L.S.D., Franca, P.M., Usberti, F.L. (2014). A
redistricting problem applied to meter reading in power
distribution networks. Computers and Operations
Research, 41(1), 65-75.
Amorim, P., Belo-Filho, M.A.F., Toledo, F.M.B., Almeder,
C., Almada-Lobo, B. (2013). Lot sizing versus batching in
the production and distribution planning of perishable
goods. International Journal of Production Economics,
146(1), 208-218.
Azi, N., Gendreau, M., Potvin, J.-Y. (2014). An adaptive
large neighborhood search for a vehicle routing problem
with multiple routes. Computers and Operations Research,
41(1), 65-75.
Anderson, R.M., Clemen, R. (2013). Toward an improved
methodology to construct and reconcile decision analytic
preference judgments. Decision Analysis, 10(2), 121-134.
Aydemir-Karadag, A., Turkbey, O. (2013). Multi-objective
optimization of stochastic disassembly line balancing with
station paralleling. Computers and Industrial Engineering,
65(3), 413-425.
Andersson, J., Jörnsten, K., Nonås, S.L., Sandal, L., Ubøe,
J. (2013). A maximum entropy approach to the
newsvendor problem with partial information. European
Journal of Operational Research, 228(1), 190-200.
Bacchetti, A., Plebani, F., Saccani, N., Syntetos, A.A.
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Page 19
stock keeping units. International Journal of Production
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547-560.
Bai, C., Sarkis, J. (2013). A grey-based DEMATEL model
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Zhen, Q., Knessl, C. (2013). On spectral properties of
finite population processor shared queues. Mathematical
Methods of Operations Research, 77(2), 147-176.
A World Wide Web site for the EURO Working Group on
“Multicriteria Aid for Decisions” is available at the URL:
Zheng, F., Cheng, Y., Xu, Y., and Liu, M. (2013).
Competitive strategies for an online generalized
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Zheng, Y.-J., Ling, H.-F., Shi, H.-H., Chen, H.-S., Chen,
S.-Y. (2014). Emergency railway wagon scheduling by
hybrid biogeography-based optimization. Computers and
Zhong, J.-H., Shen, M., Zhang, J., Chung, H.S.-H., Shi, Y.H., Li, Y. (2013). A differential evolution algorithm with
dual populations for solving periodic railway timetable
scheduling problem. IEEE Transactions on Evolutionary
Computation, 17(4), 512-527.
Zhou, F., Blocher, J.D., Hu, X., Sebastian Heese, H.
(2014). Optimal single machine scheduling of products
with components and changeover cost. European Journal
Zhou, J., Love, P.E.D., Wang, X., Teo, K.L., Irani, Z.
(2013). A review of methods and algorithms for optimizing
construction scheduling. Journal of the Operational
Zhou, L., Chen, H. (2013). The induced linguistic
continuous ordered weighted geometric operator and its
application to group decision making. Computers and
Zhou, L., Chen, H., Liu, J. (2013). Continuous Ordered
Weighted Distance Measure and Its Application to
Multiple Attribute Group Decision Making. Group
Decision and Negotiation, 22(4), 739-758.
Page 43

Series 3, Number 28, Fall 2013.

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